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ExpNumViz() function

As explained in Statistics Slam Dunk

The ExpNumViz() function from SmartEDA generates visualizations of numeric variables, including density plots and scatterplots.

ExpNumViz() uses SmartEDA to visualize numeric variables during exploratory data analysis. It returns density plots by default, helping reveal distributions and skewness. section 17.4.3

A practical workflow for inspecting numeric NBA betting data:

  • Use Page = c(2, 2) and sample = 4 to arrange four randomly selected density plots in a 2 × 2 matrix. section 17.4.3
  • Read the displayed skewness and kurtosis: values near zero support approximate normality, while larger positive or negative values indicate skew. section 17.4.3

Compare betting variables

For the NBA point-spread data, density plots showed openspreadH was approximately normally distributed, while diff_margin_closespreadH was not. Pass scatter = TRUE to request numeric-variable scatterplots instead. section 17.4.3

How Statistics Slam Dunk covers it

How deep it goes
Generates density or scatterplot panels and reports skewness and kurtosis, but does not provide a correlation matrix.
Examples
Uses openspreadH and diff_margin_closespreadH from NBA point-spread data.
What it assumes
Assumes a data frame containing numeric variables and basic familiarity with R function arguments.
What you can do after
Apply it via SmartEDA to inspect numeric distributions and visualize pairwise relationships.
How it connects
Connects density plots with QQ plots, skewness, kurtosis, and scatterplots.
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.